thelper 0.3.8

Training framework & tools for PyTorch-based machine learning projects.

This package provides a training framework and CLI for PyTorch-based machine learning projects. This is free software distributed under the Apache Software License version 2.0 built by researchers and developers from the Centre de Recherche Informatique de Montréal / Computer Research Institute of Montreal (CRIM).

To get a general idea of what this framework can be used for, visit the FAQ page. For installation instructions, refer to the installation guide. For usage instructions, refer to the user guide. The auto-generated documentation is available via readthedocs.io.

Notes

Development is still on-going — the API and internal classes may change in the future.

The project’s structure was originally generated by cookiecutter via ionelmc’s template.

Changelog

0.3.8 (2019/08/08)

• Fixed nn modules constructor args forwarding

• Updated class importer to allow parsing of non-package dirs

• Fixed file-based logging from submodules (e.g. for all data)

• Cleaned and API-fied the CLI entrypoints for external use

0.3.7 (2019/07/31)

• Fixed travis timeouts on long deploy operations

• Added output path to trainer callback impls

• Added new draw-and-save display callback

• Added togray/tocolor transformation operations

• Cleaned up matplotlib use and show/block across draw functions

• Fixed various dependency and logging issues

0.3.6 (2019/07/26)

• Fixed torch version checks in custom default collate impl

• Fixed bbox predictions forwarding and evaluation in objdetect

• Refactored metrics/callbacks to clean up trainer impls

• Added pretrained opt to default resnet impl

• Fixed objdetect trainer display and prediction callbacks

0.3.5 (2019/07/23)

• Refactored metrics/consumers into separate interfaces

• Added unit tests for all metrics/prediction consumers

• Updated trainer callback signatures to include more data

• Updated install doc with links to anaconda/docker hubs

• Cleaned drawing functions args wrt callback refactoring

• Added eval module to optim w/ pascalvoc evaluation funcs

0.3.4 (2019/07/12)

• Fixed issues when reloading objdet model checkpoints

• Fixed issues when trying to use missing color maps

• Fixed backward compat issues when reloading old tasks

• Cleaned up object detection drawing utilities

0.3.3 (2019/07/09)

• Fixed travis conda build dependencies & channels

0.3.2 (2019/07/05)

• Update documentation use cases (model export) & faq

• Cleanup module base class config backup

• Fixed docker build and automated it via travis

0.3.0 - 0.3.1 (2019/06/12)

• Added dockerfile for containerized builds

• Added object detection task & trainer implementations

• Added CLI model/checkpoint export support

• Added CLI dataset splitting/HDF5 support

• Added baseline superresolution implementations

• Added lots of new unit tests & docstrings

• Cleaned up transform & display operations

0.2.8 (2019/03/17)

• Cleaned up build tools & docstrings throughout api

• Added user guide in documentation build

• Update tasks to allow dataset interface override

• Cleaned up trainer output logs

• Added fully convolutional resnet implementation

• Fixup various issues related to fine-tuning via ‘resume’

0.2.7 (2019/02/04)

• Updated conda build recipe for python variants w/ auto upload

0.2.6 (2019/01/31)

• Added framework checkpoint/configuration migration utilities

• Fixed minor config parsing backward compatibility issues

• Fixed minor bugs related to query & drawing utilities

0.2.2 - 0.2.5 (2019/01/29)

• Fixed travis-ci matrix configuration

• Added travis-ci deployment step for pypi

• Fixed readthedocs documentation building

• Updated readme shields & front page look

• Cleaned up cli module entrypoint

• Fixed openssl dependency issues for travis tox check jobs

• Updated travis post-deploy to try to fix conda packaging (wip)

0.2.1 (2019/01/24)

• Added typedef module & cleaned up parameter inspections

• Cleaned up all drawing utils & added callback support to trainers

• Added support for albumentation pipelines via wrapper

• Updated all trainers/schedulers to rely on 0-based indexing

• Updated travis/rtd configs for auto-deploy & 3.6 support

0.2.0 (2019/01/15)

• Added regression/segmentation tasks and trainers

• Added interface for pascalvoc dataset

• Refactored data loaders/parsers and cleaned up data package

• Added lots of new utilities in base trainer implementation

• Added new unit tests for transformations

• Refactored transformations to use wrappers for augments/lists

• Added new samplers with dataset scaling support

• Added baseline implementation for FCN32s

• Added mae/mse metrics implementations

• Added trainer support for loss computation via external members

• Added utils to download/verify/extract files

0.1.1 (2019/01/14)

• Minor fixups and updates for CCFB02 compatibility

• Added RawPredictions metric to fetch data from trainers

0.1.0 (2018/11/28)

• Fixed readthedocs sphinx auto-build w/ mocking.

• Refactored package structure to avoid env issues.

• Rewrote seeding to allow 100% reproducible sessions.

• Cleaned up config file parameter lists.

• Cleaned up session output vars/logs/images.

• Add support for eval-time augmentation.

• Update transform wrappers for multi-channels & lists.

• Add gui module w/ basic segmentation annotation tool.

• Refactored task interfaces to allow merging.

• Simplified model fine-tuning via checkpoints.

0.0.2 (2018/10/18)

• Completed first documentation pass.

• Fixed travis/rtfd builds.

• Fixed device mapping/loading issues.

0.0.1 (2018/10/03)

• Initial release (work in progress).